Pandas is an excellent Python librarie to manage data. Matplotlib allows to create advanced charts and Openpyxl is very usefull to read/write Excel files. These 3 tools, combined with other classic Python features, allow to do data analysis and engineering.

First install or check Python and Pip, then the 3 libraries:

pip install pandas
pip install matplotlib
pip install openpyxl

Read data file (CSV, Excel, MySQL, Postgres, JSON, Shape ...)

Read and display an Excel file

import pandas as pd
from tabulate import tabulate
 
df = pd.read_excel('C:/Users/Your file.xlsx',
                   sheet_name='Export', engine='openpyxl', usecols=['My first column', 'My second column'])
 
print(tabulate(df.head(35), headers='keys', tablefmt='psql', showindex=True))

I set engine='openpyxl' because it is from an XLSX file. sheet_name specifies the sheet to import, specify your columns with usecols.

Then I use tabulate to display properly my data in a Postgres way (tablefmt='psql').

With head(35) I just display the 35 first lines, but the dataframe here named df contains all the data.

If you wan to re-organize the columns, please see the Miscellaneous chapter.

Read a CSV file

import pandas as pd
from tabulate import tabulate
 
df_subscriber = pd.read_csv(workDirectory+'joo_acymailing_subscriber.csv', sep=';', usecols=['subid', 'source', 'email'])
 
print(tab(df_subscriber.head(10), headers='keys', tablefmt='psql', showindex=False))

Add textual column names from a CSV without column names

Use names=[] :

import pandas as pd
from tabulate import tabulate
 
df = pd.read_csv(inpuMyFilePath, names=['Mon Champ 1', 'Mon Champ 2', 'Mon Champ 3'], sep=',', keep_default_na=False).astype(str)
 
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=False))

Read a non-standard Excel file

Sometimes a file has wrong top lines, or you can not trust the future column names.

To read a file from a specific row for example, skipping the last rows.

import pandas as pd
from tabulate import tabulate
 
df = pd.read_excel(r'C:/Users/Georges/Downloads/test excel.xlsx',
sheet_name='Sheet1', engine='openpyxl', usecols=[0,1], header=5-1, skipfooter=1, names=['id', 'value'])
 
print(tabulate(df.head(35), headers='keys', tablefmt='psql', showindex=False))

Or skip a specific line (with skiprows=[...]):

...
df = pd.read_excel(my_file, sheet_name='Feuil1', engine='openpyxl', skiprows=[2])
...

Use first row as label column

my_labels = df.iloc[0]
df = df[1:]
df.columns = my_labels

Read from MySQL

Installing sqlalchemy (the best way with pandas):

from tabulate import tabulate as tab
import pandas as pd
from sqlalchemy import create_engine
 
username = 'root'
password = ''
port = 3308
database = 'my_beautiful_db'
host ='localhost'
 
engine = create_engine('mysql+mysqldb://%s:%s@%s:%i/%s' % (username, password, host, port, database))
sql = 'SELECT COUNT(*) AS total FROM communes ;'
df = pd.read_sql_query(sql, engine).set_index('total')
 
print(tab(df.head(5), headers='keys', tablefmt='psql'))

Specify database encoding

engine = create_engine('mysql+mysqldb://%s:%s@%s:%i/%s?charset=utf8mb4' % (username, password, host, port, database))

Or installing mysqlclient:

import MySQLdb
from tabulate import tabulate
import pandas as pd
 
mydb = MySQLdb.connect(user='root', password='', database='MyDb', host='localhost', port=3306)
query = 'SELECT name FROM users ;'
 
df = pd.read_sql(query, mydb)
mydb.close()
 
print(tabulate(df.head(10), headers='keys', tablefmt='psql'))
number = df.shape[0]
print(number)

Or below, installing mysql.connector:

import mysql.connector as connection
import pandas as pd
from tabulate import tabulate as tab
 
mydb = connection.connect(host='XXX.XX.XXX.XX', port=3308, database='MyDb',user='Me', passwd='pwdXXX!456', use_pure=True)
 
try:
    query = "SELECT firstname, lastname FROM customer ;"
    df = pd.read_sql(query, mydb)
except Exception as e:
    mydb.close()
    print(str(e))
 
print(tab(df.head(5), headers='keys', tablefmt='psql'))

Read from Postgres

import pandas as pd
from tabulate import tabulate
import psycopg2
 
con = psycopg2.connect(database='mytransformations', user='postgres', password='', host='localhost', port='5432')
print("OK, we are connect to mytransformations")
 
# DISPLAY WITH PANDAS
df = pd.read_sql("SELECT * FROM myparcels ;", con)
df.drop('geom', axis=1, inplace=True)
 
print('\nResults:')
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=False))
print('Number of records:', df.shape[0])

Read from a JSON

import pandas as pd
from tabulate import tabulate
import json
import requests
 
response = requests.get(MON_URL)
my_file = json.loads(response.text)
my_json = my_file['data']
 
mon_df = pd.DataFrame.from_dict(my_json)

Read from a shape

Use geopandas:

import geopandas as gpd
from tabulate import tabulate
 
myShape = 'C:\\Path\\Of\\My\\Shape.shp'
 
print('\n' + myShape)
 
df = gpd.read_file(myShape)
df['type'] = df['geometry'].astype(str).str.replace(r' .*|\(.*', '', regex=True)
df = df[['id', 'type', 'geometry']]
 
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=True))
print(df.shape[0])
 
MyFieldList = df['id'].drop_duplicates().dropna().sort_values(ascending=False).tolist()
print('\n' + str(MyFieldList))
 
MyGeomTypeList = df['type'].drop_duplicates().dropna().sort_values(ascending=False).tolist()
print('\n' + str(MyGeomTypeList))

Export a data-frame in a new Excel file

df.to_excel("C:/Users/Your new file.xlsx", index=False, sheet_name='Tab 1')

index=False to not get the index column.

Export a data-frame in an existing Excel sheet

Use mode='a' and if_sheet_exists:

from openpyxl import load_workbook
import openpyxl
 
My_File_Path= 'C:/My/File.xlsx'
 
with pd.ExcelWriter(My_File_Path, 'openpyxl', mode='a', if_sheet_exists='replace') as writing:
    df.to_excel(writing, 'My sheet', index=False)

Export several dataframes in Excel sheets

If you have several data-frame to export in one Excel file, each in unique sheets, you should open an ExcelWriter. And close it.

writer = pd.ExcelWriter('C:/Users/Your new file.xlsx', engine='xlsxwriter')
 
df_Country.to_excel(writer, index=False, sheet_name='Country', header=['Country', 'Total'])
df_Emails.to_excel(writer, index=False, sheet_name='Emails', header=['Emails', 'Total'])
 
writer.save()

Export several dataframes in the same sheet

df.to_excel(writer, index=False, sheet_name='Registrations', startcol=3, startrow=34, header=['Filed', 'Total', '%'])

Export a dataframe as a txt file, with separator and ANSI encoding

df.to_csv(my_file_import, header=None, index=None, sep='\t', encoding='mbcs')

Replace a string with a dataframe in an existing txt file

Do a replace with a df.to_string(), maybe with a little regex to replace unwanted spaces.

with open(MtTxtFile, 'r') as file:
    MyTxtData = MyTxtData.replace('STRING TO REPLACE', df.to_string(header=False, index=False, justify='left'))
    MyTxtData = re.sub(r'^ *', '', MyTxtData, flags=re.MULTILINE)
 
with open(MtTxtFile, 'w') as file:
    file.write(MyTxtData)

Add filters in all columns in one sheet in an Excel file

import openpyxl
from openpyxl.utils import get_column_letter
 
outputExcelFile = r'C:/User/Your file.xlsx'
workbook = openpyxl.load_workbook(outputExcelFile)
 
worksheet = workbook['Sheet name']
FullRange = 'A1:' + get_column_letter(worksheet.max_column) + str(worksheet.max_row)
worksheet.auto_filter.ref = FullRange
 
workbook.save(outputExcelFile)

Add filters in all columns in all sheets in an Excel file

import openpyxl
from openpyxl.utils import get_column_letter
 
outputExcelFile = r'C:/User/Your file.xlsx'
 
workbook = openpyxl.load_workbook(outputExcelFile)
sheetsLits = workbook.sheetnames
 
for sheet in sheetsLits:
    worksheet = workbook[sheet]
    FullRange = 'A1:' + get_column_letter(worksheet.max_column) + str(worksheet.max_row)
    worksheet.auto_filter.ref = FullRange
    workbook.save(outputExcelFile)

Add colors in all column names in a sheet in an Excel file

import openpyxl
from openpyxl.styles import PatternFill
 
workbook = openpyxl.load_workbook(My_File_Path)
worksheet = workbook['My sheet']
 
for cell in worksheet[1]:
    worksheet[cell.coordinate].fill = PatternFill(fgColor='FFC6C1C1', fill_type='solid')
    workbook.save(My_File_Path)

Add colors in all column names in all sheets in an Excel file

import openpyxl
from openpyxl.styles import PatternFill
 
outputExcelFile = r'C:/Users/Your File.xlsx'
 
workbook = openpyxl.load_workbook(outputExcelFile)
sheetsLits = workbook.sheetnames
 
for sheet in sheetsLits:
    worksheet = workbook[sheet]
    for cell in workbook[sheet][1]:
        worksheet[cell.coordinate].fill = PatternFill(fgColor = 'FFC6C1C1', fill_type = 'solid')
        workbook.save(outputExcelFile)

Modify a column width in one sheet in an Excel file

import openpyxl
 
outputExcelFile = r'C:/Users/Your File.xlsx'
 
workbook = openpyxl.load_workbook(outputExcelFile)
sheetsLits = workbook.sheetnames
 
workbook['Your sheet'].column_dimensions['A'].width = 75
workbook.save(outputExcelFile)

Modify all columns width in all sheets in an Excel file

The code below set the A column size to 30, and the others to 10.

import openpyxl
from openpyxl.utils import get_column_letter
 
outputExcelFile = r'C:/Users/Your File.xlsx'
 
workbook = openpyxl.load_workbook(outputExcelFile)
sheetsLits = workbook.sheetnames
 
for sheet in sheetsLits:
    for cell in workbook[sheet][1]:
        if get_column_letter(cell.column) == 'A':
            workbook[sheet].column_dimensions[get_column_letter(cell.column)].width = 30
        else:
            workbook[sheet].column_dimensions[get_column_letter(cell.column)].width = 10
 
        workbook.save(outputExcelFile)

Add color to some line recovering their indexes from condition

list_duplicate = df.index[df['Ligne en doublon'] == 'DOUBLON !'].tolist()
 
for index in list_duplicate:
    for cell in worksheet[index+2]:
        worksheet[cell.coordinate].fill = PatternFill(fgColor='FF6666', fill_type='solid')
        workbook.save(My_File_Path)

Freeze the top row of an Excel file

worksheet.freeze_panes = 'A2'

Freeze the top row and the first column of an Excel file

worksheet.freeze_panes = 'B2'

Freeze the top row and the 2 first columns of an Excel file

worksheet.freeze_panes = 'C2'

Change font color and style in an Excel file

from openpyxl.styles import Font
 
worksheet['A1'].font = Font(color='FFFFFF', italic=False, bold=True)

Work on several Excel files

Here we freeze the top row, add filters, color and change the width of all columns from 3 Excel files. 

import openpyxl
from openpyxl.utils import get_column_letter
from openpyxl.styles import PatternFill
 
 
File1 = r'D:/Work/My file 1.xlsx'
File2 = r'D:/Work/My file 2.xlsx'
File3 = r'D:/Work/My file 3.xlsx'
 
ExcelList = [File1, File2, File3]
 
for ExcelFile in ExcelList:
    workbook = openpyxl.load_workbook(ExcelFile)
    sheetsLits = workbook.sheetnames
 
    for sheet in sheetsLits:
        worksheet = workbook[sheet]
        FullRange = 'A1:' + get_column_letter(worksheet.max_column) + str(worksheet.max_row)
        worksheet.auto_filter.ref = FullRange
        worksheet.freeze_panes = 'A2'
        for cell in workbook[sheet][1]:
            worksheet[cell.coordinate].fill = PatternFill(fgColor='FFC6C1C1', fill_type='solid')
            if get_column_letter(cell.column) == 'A':
                workbook[sheet].column_dimensions[get_column_letter(cell.column)].width = 12
            else:
                workbook[sheet].column_dimensions[get_column_letter(cell.column)].width = 20
            workbook.save(ExcelFile)

Insert a picture in an Excel file

And delete them after.

img = openpyxl.drawing.image.Image('C:/Users/myplot.png')
img.anchor = 'E6'
 
workbook['Your sheet'].add_image(img)
workbook.save(outputExcelFile)
 
os.remove('C:/Users/myplot.png')

Open a box to search a file and get his path

Use Tkinter.

from tkinter import Tk
from tkinter.filedialog import askopenfilename
 
Tk().withdraw()
filename = askopenfilename()
print(filename)

Open a box to save an Excel file

from tkinter import Tk
from tkinter.filedialog import asksaveasfilename
 
fileName = asksaveasfilename(filetypes=(("Excel files", "*.xlsx"), ("All files", "*.*")), defaultextension='.xslx')
df.to_excel(fileName, index=False, sheet_name='Tab 1', header=None)

Check if a file exists

And use it only if it exists:

import os
 
my_file = 'C:/_gh/0/export.xlsx'
if os.path.isfile(my_file) == True:
    df_ExportFeuil1 = pd.read_excel(my_file, sheet_name='Feuil1', engine='openpyxl')

Rename a file

import os
os.rename('My file.xlsx', 'My file renamed.xlsx')

 

 

Get dataframe from lists

One list, one field

import pandas as pd
from tabulate import tabulate
 
print('\nONE LIST, ONE FIELD')
MyList = ['7878', 'Bbbb', 'azerfg', '545', 'XXX']
df = pd.DataFrame(MyList)
df.columns = ['My field']
number = df.shape[0]
 
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=False))
print(number)

Several lists in their own field, each list with same lenght

import pandas as pd
from tabulate import tabulate
 
print('\nSEVERAL LISTS IN THEIR OWN FIELD')
print('EACH LIST WITH SAME LENGHT')
MyList1 = ['7878', 'Bbbb', 'azerfg', '545', 'XXX']
MyList2 = ['Bruno', 'Constance', 'Mathieu', 'Abbes', 'Georges']
 
df = pd.DataFrame(
{'My field 1': MyList1,
'My field 2': MyList2
})
 
number = df.shape[0]
 
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=False))
print(number)

Several lists in the same field, lists with any lenght

import pandas as pd
from tabulate import tabulate
 
print('\nSEVERAL LISTS IN THE SAME FIELD')
print('LISTS WITH ANY LENGHT')
MyList1 = ['7878', 'Bbbb', 'azerfg', '545', 'XXX']
MyList2 = ['Bruno', 'Constance', 'Mathieu', 'Abbes']
 
df = pd.DataFrame(MyList1 + MyList2)
df.columns = ['My field']
number = df.shape[0]
 
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=False))
print(number)

Several lists from dictionaries, each list element as a field, lists with any lenght

import pandas as pd
from tabulate import tabulate
 
print('\nSEVERAL LISTS FROM DICTIONARIES')
print('EACH LIST ELEMENT AS A FIELD')
print('LISTS WITH ANY LENGHT')
MyList1 = ['Bruno', '11111', 'Rouge']
MyList2 = ['Constance', '22222', 'Jaune']
MyList3 = ['Mathieu', '33333', 'Bleu']
MyList4 = ['Abbes', '44444']
 
df = pd.DataFrame([MyList1] + [MyList2] + [MyList3] + [MyList4])
df.columns = ['My field 1', 'My field 2', 'My field 3']
number = df.shape[0]
 
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=False))
print(number)

A list from several lists from dictionaries, each sub-list as a row, each element from sub-list as a field, lists with any lenght

import pandas as pd
from tabulate import tabulate
 
print('\n LIST FROM SEVERAL LISTS FROM DICTIONARIES')
print('EACH SUB-LIST AS A ROW')
print('EACH ELEMENT FROM SUB-LIST AS A FIELD')
print('LISTS WITH ANY LENGHT')
 
MyList = [
    ['Bruno', '11111', 'Rouge'],
    ['Constance', '22222', 'Jaune'],
    ['Mathieu', '33333', 'Bleu'],
    ['Abbes', '44444']
]
 
df = pd.DataFrame(columns=['My field 1', 'My field 2', 'My field 3'], data=MyList)
 
number = df.shape[0]
 
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=False))
print(number)

 

Work with lists

Here below is not right about Pandas, but usefull in some Pandas contexts.

Search if elements from a list are in another list

Chek if elements from List2 are in List1:

import collections
 
List1 = ['Aaa', 'Bbbb', 'Cccc', 'Dddd', 'Eeee', 'Ffff', 'Gggg', 'Hhhh']
List2 = ['Eeee', 'Cccc']
 
print(List1)
print(List2)
 
check = all(item in List1 for item in List2)
 
print(check)

 Or:

import collections
 
List1 = ['Aaa', 'Bbbb', 'Cccc', 'Dddd', 'Eeee', 'Ffff', 'Gggg', 'Hhhh']
List2 = ['Bbbb', 'Aaa', 'Cccc', 'Dddd', 'Eeee', 'Ffff', 'Hhhh', 'Gggg']
 
print(List1)
print(List2)
 
def iCompare(c1, c2):
    if(collections.Counter(c1)==collections.Counter(c2)):
        return 'Yes'
    else:
        return 'No'
 
info = iCompare(List1, List2)
 
print(info)

 Extract common elements from several lists

List1 = ['Aaa', 'Aaa', 'Bbbb', 'Cccc', 'Dddd', 'Eeee', 'Ffff', 'Gggg', 'Hhhh']
List2 = ['Eeee', 'Cccc', 'Cccc']
List3 = ['Cccc', 'Bbbb', 'Eeee']
 
print('List1:', List1)
print('List2:', List2)
print('List3:', List3)
 
Compare = sorted(list(set(List1) & set(List2) & set(List3) ))
 
print('\nCompare:', Compare)

 Extract no-common elements from several lists

List1 = ['Aaa', 'Aaa', 'Bbbb', 'Cccc', 'Dddd', 'Eeee', 'Ffff', 'Gggg', 'Hhhh']
List2 = ['Eeee', 'Cccc', 'Cccc']
 
print('List1:', List1)
print('List2:', List2)
 
Compare = sorted(list(set(List1) - set(List2) ))
 
print('\nCompare:', Compare)

Concatenate 2 fields in list/sentence

Assemblage = list(zip(df['field1'].tolist(), df['field2'].tolist()))
b = []
for a in Assemblage:
    b.append('Blablabla...  ' + str(list(a)[0]) + ' ...Blablabla... ' + list(a)[1] + ' ...Blablabla.')
for i in b:
    print(i)

List all file from a directory in a Python list

repertoireCheck = 'E:/_CheckTables/'
 
listTxtFile = []
 
for path, subdirs, files in os.walk(repertoireCheck):
    for name in files:
        print(os.path.join(path, name))
        listTxtFile.append(os.path.join(path, name))
 
print(listTxtFile)
print(len(listTxtFile))

Split a big list in smaller sub-lists (1)

SubListsSize = 750
SubLists = [MyBigList[x:x+SubListsSize] for x in range(0, len(MyBigList), SubListsSize)]
 
for SubList in SubLists:
    print(SubList)

Split a big list in smaller sub-lists (2)

def split_list(my_list, size_max):
    sublists = []
    for i in range(0, len(my_list),size_max):
       sublist = liste[i:i+size_max]
       sublists.append(sublist)
    return sublists
 
sublists = split_list(MyPersonalList, 10)

 

Advanced read/write in Excel

Formulas

Write formula

No problem to create Excel formulas with Pandas, but use these tips below.

• As you will use simple and double quote in your formulas, frame your string with triple quotes.
• Translate in english all Excel keywords (IF, CHAR, SUBSTITUTE...).
• Use a simple comma to separate your parameters (english...).
• Re-sort your dataframe and reset your index before the formula:

df.sort_values(by=['Your Id'], ascending=True, inplace=True)
df = df.reset_index(drop=True)

• Start the index with a special value (to start with 2, as usual in Excel):

df.index = df.index + 2

• Then use the index as a string in your formula:

'''...''' + df.index.astype(str) + '''...'''

• Do not add other sorts after the formulas creation, it causes index issues and impacts your formulas.

 

Read formula

Sometime you want read formula as string. If the Excel file was created by Excel himself, no problem, just use data_only=True:

wb = openpyxl.load_workbook('My file.xlsx', data_only = True)
ws = wb['My tab']
df = pd.DataFrame(ws.values)
print(tabulate(df.head(5), headers='keys', tablefmt='psql', showindex=False))

But if the file, and the formulas, was created with Python, so the Excel cache do not know your formulas and... oups!

So just open and close your file before to use data_only=True:

import xlwings
...
excel_app = xlwings.App(visible=False)
excel_book = excel_app.books.open('My file.xlsx')
excel_book.save()
excel_book.close()
excel_app.quit()

Conditional formatting

If a number is equal to, in just one cell

Here we add in one cell (B3) a rule to fill it in blue if it equals to 2:

import openpyxl
from openpyxl import styles, formatting
 
myFile = 'C:/Path/test.xlsx'
 
myDF.to_excel(myFile, index=False, sheet_name='Tab 1')
 
workbook = openpyxl.load_workbook(myFile)
worksheet = workbook['Sheet1']
 
myBlue = styles.PatternFill(start_color='0088cc', end_color='0088cc', fill_type='solid')
worksheet.conditional_formatting.add('B3', formatting.rule.CellIsRule(operator='equal', formula=['2'], fill=myBlue))
 
workbook.save(myFile)

If a number is greater than, in whole column

Same as above, we just change the operator with greaterThan and we target the whole column B as Excel did, with  $1:$1048576:

import openpyxl
from openpyxl import styles, formatting
 
myFile = 'C:/Path/test.xlsx'
 
myDF.to_excel(myFile, index=False, sheet_name='Tab 1')
 
workbook = openpyxl.load_workbook(myFile)
worksheet = workbook['Sheet1']
 
myBlue = styles.PatternFill(start_color='0088cc', end_color='0088cc', fill_type='solid')
worksheet.conditional_formatting.add('B1:B1048576', formatting.rule.CellIsRule(operator='greaterThan', formula=['2'], fill=myBlue))
 
workbook.save(myFile)

Operators

The above method supports several operators:

{'beginsWith', 'endsWith', 'notBetween', 'notContains', 'lessThan', 'greaterThanOrEqual', 'notEqual', 'between', 'lessThanOrEqual', 'containsText', 'greaterThan', 'equal'}

If contains a string

For match with strings it is a little specific, with redundant code and building the column_slot ourself, using the size of our dataframe +1 :

Here for example we fill in red the cells from A column when contain the string CSS.

import pandas as pd
import openpyxl
from openpyxl import styles, formatting
from openpyxl.styles.differential import DifferentialStyle
from openpyxl.formatting.rule import Rule
 
myFile = 'C:/Path/test.xlsx'
 
myDF.to_excel(myFile, index=False, sheet_name='Tab 1')
 
workbook = openpyxl.load_workbook(myFile)
worksheet = workbook['Sheet1']
 
myRed = styles.PatternFill(start_color='ffc7ce', end_color='ffc7ce', fill_type='solid')
 
column_slot = 'A2:A'+str(myDF.shape[0]+1)
 
styleRed = DifferentialStyle(fill=myRed)
rule = Rule(type='containsText', operator='containsText', text='css', dxf=styleRed)
rule.formula = ['SEARCH("css",' + column_slot + ")"]
worksheet.conditional_formatting.add(column_slot, rule)
 
workbook.save(myFile)

Fix Excel percentage

When your data comes from an Excel file and contains percentages, something like dtype (search it in Miscellaneous chapter) will be non-efficient, because Excel stores percentages as numbers (indices between 0 and 1). The displaying with % is just a view. Actually 35% will be stored as 0.3 for example.

In addition, it is possible that these percentages are melted with true strings or numbers in the same columns, and sometimes with or without decimal ... Argghhh 🥵

But just relax:

import re
...
df[MyField] = df[MyField].apply(lambda x: str(round(float(x) * 100, 2)).rstrip('0').rstrip('.') + ' %' if re.search(r'^0\.\d+$|^0$|^1$',x) else x)

Attention

If the Excel file is wrong builded, melting true numbers between 0 and 1 with percentages, they will be converted. So maybe add red alerts to manage these special cases.

 

Advanced write in a TXT file

Split file according to lines numbers

Here below is not right about Pandas, but usefull in some Pandas contexts.

First we count the number of lines in the file (from the recurrence of \n), then we split it in files with 50,000 lines if it exceeds 50,000 lines.

with open(workDirectory+'FixQueries.sql', 'r') as myfile:
data = myfile.read()
taille_fichier = data.count("\n")
 
lines_max = 50000
numero_fichier = 0
if taille_fichier > lines_max:
    print('Attention : le fichier de sortie fait '+str(taille_fichier)+' lignes ! Veuillez patienter...')
    smallfile = None
    with open(workDirectory+'FixQueries.sql') as bigfile:
        for lineno, line in enumerate(bigfile):
            if lineno % lines_max == 0:
                if smallfile:
                    smallfile.close()
                numero_fichier += 1
                small_filename = workDirectory + 'FixQueries {}.sql'.format(numero_fichier)
                smallfile = open(small_filename, "w")
            smallfile.write(line)
        if smallfile:
            smallfile.close()
            print('Nous l\'avons découpé en ', ceil(taille_fichier/lines_max), 'fichiers !\n')
    file1.close()
    os.remove(workDirectory+'FixQueries.sql')

 And then merge the files:

filenames = ['C:/_gh/0/file_25000.txt', 'C:/_gh/0/file_50000.txt', 'C:/_gh/0/file_75000.txt', 'C:/_gh/0/file_100000.txt', 'C:/_gh/0/file_125000.txt']
 
with open('C:/_gh/0/CUMUL1.txt', 'w') as outfile:
    for names in filenames:
        with open(names) as infile:
            outfile.write(infile.read())

 Maybe you will need to list the names of the files before to merge them, with PowerShell (Alt+F+R):

get-childitem | select-object -expandproperty name > _files_list.txt

Attention 👈

Be careful to keep the order of the file, to keep the last line empty.

Replace text in a file

my_file = r'C:/_gh/0/_Urbanisme/test.txt'
 
replacements = [
    ('heros', 'héros'),
    ('Batm?n', 'Batman'),
    ('télévisee', 'télévisée'),
    (' s attaquent', ' s\'attaquent')
]
 
 
with open(my_file, 'r') as file:
    data = file.read()
 
    for a, b in replacements:
        data = data.replace(a, b)
 
with open(my_file, 'w') as file:
    file.write(data)
 
# Confirm
print("OK !")

This is not right related to Pandas, but it is very useful to store some special note to remember during a data process, and automate it.

Create/replace a TXT file and write something into

Use the Write option (w):

MyTxtFile = 'C:/_gh/0/My beautiful file.txt'
 
with open(MyTxtFile, 'w') as file:
    file.write('Blablabla...\n')
    file.write(MyVariable)

Add some lines at the start of an existing file

MyTxtFile = 'C:/_gh/0/My beautiful file.txt'
 
with open(MyTxtFile, 'r') as original: data = original.read()
with open(MyTxtFile, 'w') as modified: modified.write('XPTDR\n' + data)

Add some lines at the end of an existing file

Use the Append option (a):

MyTxtFile = 'C:/_gh/0/My beautiful file.txt'
 
with open(MyTxtFile, 'a') as file:
    file.write('Blablabla...\n')
    file.write(MyVariable)

Replace some lines in an existing file

Here we first Read (r option) and store the content of the file, and then re-create it removing the lines we would delete:

MyTxtFile = 'C:/_gh/0/My beautiful file.txt'
 
with open(MyTxtFile, 'r') as f:
    lines = f.readlines()
 
with open(MyTxtFile, 'w') as f:
    for line in lines:
        if not line.strip('\n').startswith('First line to delete...') :
            if not line.strip('\n').startswith('Second line to delete...') :
                if not line.strip('\n').startswith('Third line to delete...') :
                    f.write(line)
 
with open(MyTxtFile, 'a') as file:
    file.write('New line 1')
    file.write('New line 2')
    file.write('New line 3')

Replace a simple string in a TXT file

MyTxtFile = 'C:/_gh/0/My beautiful file.txt'
 
with open(MyTxtFile, 'r') as file :
    filedata = file.read()
 
filedata = filedata.replace('String 1', 'String 2')
 
with open(MyTxtFile, 'w') as file:
    file.write(filedata)

Replace a string with a regular expression (regex) in a TXT file

FichierTest = 'C:/_gh/0/FichierTest.txt'
 
import re
 
# Find number : r'[0-9]'
# Find line break : r'[\n]'
# Find double line break : r'[\r\n]{2,}'
 
with open(FichierTest, 'r+') as file:
    text = file.read()
    text = re.sub(r'[\r\n]{2,}', '\n\n', text)
    file.seek(0, 0) # seek to beginning
    file.write(text)
    file.truncate() # get rid of any trailing characters

Merge 2 txt files

MyTxtFile1 = 'C:/_gh/0/My beautiful file 1.txt'
MyTxtFile2 = 'C:/_gh/0/My beautiful file 2.txt'
MyTxtFileMerged = 'C:/_gh/0/My beautiful file merged.txt'
 
with open(MyTxtFile1, 'r') as _MyTxtFile1: dataMyTxtFile1 = _MyTxtFile1.read()
with open(MyTxtFile2, 'r') as _MyTxtFile2: dataMyTxtFile2 = _MyTxtFile2.read()
 
with open(MyTxtFileMerged, 'w', encoding='utf-8') as _MyTxtFileMerged:
_MyTxtFileMerged.write(dataMyTxtFile1)
_MyTxtFileMerged.write(dataMyTxtFile2)
 
os.remove(MyTxtFile1)
os.remove(MyTxtFile2)

Test if a comma is used as decimal separator in a TXT file

with open(MyTxtFile, 'r') as myfile:
    data = myfile.read()
    pb_regex = re.findall(r'(?:\t|^)\d+\K,(?=\d+(?:\t|$))', data)
    if pb_regex:
        print(colored('\nAttention, comma is used as a decimal separator in numeric value!\n' +
        'Find them in Notepad with:\n(?:\\t|^)\d+\K,(?=\d+(?:\\t|$))', 'yellow'))
    else:
        pass

Write sentences from dataframe

MyTxtFile = WorkDirectory + ' - My file.txt'
 
with open(MyTxtFile, 'w') as file:
    file.write('\nSpecial cases:\n\n')
 
    Assemblage = list(zip(df['Field1'].tolist(), df['Field2'].tolist(), df['Field3'].tolist(), df['Field4'].tolist()))
    b = []
    for a in Assemblage:
        b.append('- ' + str(list(a)[0]) + ' ' + str(list(a)[1]) + ' (' + str(list(a)[2]) + ') in ' + str(list(a)[3]))
    for i in b:
        file.write(i + '\n')

Convert encoding

With chardet:

from chardet import detect
 
def get_encoding_type(file):
    with open(file, 'rb') as f:
        rawdata = f.read()
    return detect(rawdata)['encoding']
 
from_codec = get_encoding_type(MyOriginalFile)
 
if from_codec != 'utf-8':
    print(colored('Attention, script is ' + from_codec + '!', 'yellow'))
 
    try:
        with open(, 'r', encoding=from_codec) as f, open(MyNewFile, 'w', encoding='utf-8') as e:
            text = f.read()
            e.write(text)
 
        os.remove(MyOriginalFile)
        os.rename(MyNewFile, MyOriginalFile)
 
        print(colored('We converted the file as UTF-8.', 'yellow'))
 
    except UnicodeDecodeError:
        print('Decode Error')
    except UnicodeEncodeError:
        print('Encode Error')

With io:

import io
 
with io.open(OriginalTxtFile, mode='r', encoding='utf8') as fd:
    content = fd.read()
 
with io.open(FinalTxtFile, mode='w', encoding='cp1252') as fd:
    fd.write(content)

 

Advanced read in a TXT file

Work data in a TXT file according the line number

You can grab data in a TXT file according the line number, or counting a number of lines before or after a specific string.

file = open('C:/Users/Georges/Downloads/MyFile.txt', 'r')
lines = file.readlines()
 
listSubject = []
listFirstname = []
 
for num, x in enumerate(lines):
    if x.startswith('Subject:\t'):
        listSubject.append(x)
        listFirstname.append(lines[num+6])
 
MergeLists = list(zip(listSubject, listFirstname))
 
df = pd.DataFrame(MergeLists, columns=['field Subject', 'field Firstname'])

Get encoding of a file

from chardet import detect
 
def get_encoding_type(file):
    with open(file, 'rb') as f:
        rawdata = f.read()
    return detect(rawdata)['encoding']
 
from_codec = get_encoding_type(MyFile)
 
print('from_codec')
print(from_codec)

 

Merge, concatenate, append

Merge 2 lists in 2 columns and properly display them in the terminal

import pandas as pd
from tabulate import tabulate
 
LanguageList = ['Python', 'SQL', 'PHP', 'CSS', 'Javascript', 'HTML']
LevelList = ['Fun', 'OK', 'Arghh', 'OK', 'Arghh', 'Easy']
 
LanguageLevel = list(zip(LanguageList, LevelList))
 
df = pd.DataFrame(LanguageLevel, columns =['Language', 'Level'])
 
print(tabulate(df, headers='keys', tablefmt='psql', showindex=False))

The 7th line uses zip to link the values from our lists in tuples. list encloses our tuples in a list.

The 9th line creates a data-frame (pd.DataFrame) from our new list (now containing tuples). Specifying the column labels.

The print uses tabulate to display our fields with a beautiful Postgres style (psql).

Split a dataframe with conditions, and them merge the splits according other conditions

firstSelection = df.loc[df['Field'] == 'Yes'].head(100)
 
secondSelection = df.loc[df['Field'] == 'No'].head(200)
 
# Merge the 2 selections.
df_Selections = pd.concat([firstSelection, secondSelection], axis=0)

Concatenate dataframes in a loop

df_Concatenation = pd.DataFrame()
 
for ... :
    df...
    df_Concatenation = pd.concat([df_Concatenation, df], axis=0)

Aggregate files

Imagine that you want to iterate through a directory, which itself contains other directories, to aggregate Excel files contained in each of the subdirectories:

Context:

• The main directory is here named _ForAnalysis in C.
• The name of each subdirectories contains the word typical directory.
• The files that you would to aggregate are named Typical file.xlsx.
• Each Excel file contains a sheet named Typical sheet with a Field1 and a Field2.

 Code:

from termcolor import colored
import os
import pandas as pd
from tabulate import tabulate
 
# workDirectory
workDirectory = 'C:/_ForAnalysis/'
print('\nWork directory: ' + workDirectory)
 
TypicalFile = 'Typical file.xlsx'
NumberTypicalFile = 0
 
df_CumulatedFiles = pd.DataFrame()
 
print(colored('\nCheck typical files (' + str(len(os.listdir(workDirectory))) + '):', 'blue'))
 
for rep in os.listdir(workDirectory):
    if 'typical directory' in rep:
        NumberTypicalFile += 1
 
        TypicalPath = os.path.join(workDirectory, rep)
        TypicalPath = TypicalPath + '/' + TypicalFile
 
        print(TypicalPath)
 
        df_TypicalFile = pd.read_excel(TypicalPath, sheet_name='Typical sheet', engine='openpyxl', usecols=['Field1', 'Field2'])
 
        # DISPLAY ONE BY ONE
        print(tabulate(df_TypicalFile.head(10), headers='keys', tablefmt='psql', showindex=False))
        print(str(df_TypicalFile.shape[0]), 'commune(s)')
        print('')
 
        # FILES CONCATENATION
        df_CumulatedFiles = pd.concat([df_CumulatedFiles, df_TypicalFile], axis=0)
 
print(colored('\n' + str(NumberTypicalFile) + ' file(s) have been aggregated:', 'blue'))
print(tabulate(df_CumulatedFiles.head(10), headers='keys', tablefmt='psql', showindex=False))
print(str(df_CumulatedFiles.shape[0]), 'lines')

Fill a dataframe from a list (append)

df = pd.DataFrame()
 
for l in MyList:
    dfTemp = pd.DataFrame({
        'Field 1': 'Georges',
        'Field 2': [l]
    })
 
df = pd.concat([df, dfTemp], axis=0)
 
print('\ndf :')
print(tabulate(df.head(2), headers='keys', tablefmt='psql', showindex=False))
print(df.shape[0])

 

Analyze data from a multi-valued field

Suppose you have a field containing values coming from a multi-valued drop-down list. But now you want to count how many times each value exists.

If values were uniques in each cell, a simple GROUP BY in SQL would do the job.

But here we first have to split each cell to recover unique values, then we will be able to count them.

#Pandas

Split the data

Start splitting the data with str.split specifying the separator and expanding, to create new columns to store each unique values.

import pandas as pd
 
df = pd.read_excel('C:/Users/Georges/Downloads/My Excel file.xlsx',
                   sheet_name='Sheet1', engine='openpyxl')
 
serie = df['Your field'].str.split(', ', expand=True)
 
print(serie)

Great! Now we get virtual columns containing only one value, with as much columns as necessary.

Melt the virtual data

Now let's go to melt our virtual columns in just one.

import pandas as pd
 
df = pd.read_excel('C:/Users/Georges/Downloads/My Excel file.xlsx',
                   sheet_name='Sheet1', engine='openpyxl')
 
serieMelt = pd.melt(df['Your field'].str.split(', ',expand=True))['value']
 
print(serieMelt)

str.split gives virtual column labels named value, that we can use.

Count unique values

Now just put our melted data in a new data frame, before to apply a sorted groupby by size() (a COUNT in SQL).

The full code will be:

import pandas as pd
 
df = pd.read_excel('C:/Users/Georges/Downloads/My Excel file.xlsx', sheet_name='Sheet1', engine='openpyxl')
 
temp_df = pd.DataFrame(pd.melt(df['Your field'].str.split(', ', expand=True))['value'])
 
df_Count = pd.DataFrame(temp_df.groupby(['value']).size(), columns=['Total']).reset_index()
 
print(df_Count)

We add a reset_index() to recover column names properly, to use them later.

Percentage

Ok, but suppose you want also the recurrence percentages of your unique values. The usual method will take the total from the melted data, giving strange results. Indeed the melted data cumulated each values from the multi-valued field in new rows, so a lot!

To get real percentages you have to use shape[0] to count the real number of rows from your original data-frame, and use it in the percentage calculation.

We start creating a new field in the results data-frame, then we calculate percentages, rounding them.

df_Count['Percent'] = (df_Count['Total'] / df.shape[0]) * 100
df_Count['Percent'] =df_Count['Percent'].round(decimals=2)

Of course, you can not add the percentage results, it will exceed 100% and it is normal.

Empty values management

Good. But about our empty values? Just add dropna=False to see them.

import pandas as pd
 
df = pd.read_excel('C:/Users/Georges/Downloads/My Excel file.xlsx', sheet_name='Sheet1', engine='openpyxl')
 
temp_df = pd.DataFrame(pd.melt(df['Your field'].str.split(', ', expand=True))['value'])
df_Count = pd.DataFrame(temp_df.groupby(['value'], dropna=False).size(), columns=['Total']).reset_index()
 
df_Count['Percent'] = (df_Count['Total'] / df.shape[0]) * 100
df_Count['Percent'] =df_Count['Percent'].round(decimals=2)
 
print(df_Count)

Any problem? 

Arrgg! The groupby counted empty values from the melted data, and the melted data has added new columns as many times as necessary (based on the record with the highest number of unique values). Each them containing empty values ... It skews the count and percentage.

We have to cheat to fix that.

import pandas as pd
 
df = pd.read_excel('C:/Users/Georges/Downloads/My Excel file.xlsx', sheet_name='Sheet1', engine='openpyxl')
 
temp_df = pd.DataFrame(pd.melt(df['Your field'].str.split(', ', expand=True))['value'])
df_Count = pd.DataFrame(temp_df.groupby(['value'], dropna=False).size(), columns=['Total']).reset_index()
df_Count = df_Count.fillna('AZERTY')
 
df_Count['Percent'] = (df_Count['Total'] / df.shape[0]) * 100
df_Count['Percent'] =df_Count['Percent'].round(decimals=2)
 
valuesEmpty = df['Industries:Industries'].isna().sum()
valuesEmptyPercent = round((valuesEmpty / df.shape[0]) * 100, 2)
 
df_Count.loc[(df_Count['value'] == 'AZERTY')] = [['Unknow', valuesEmpty, valuesEmptyPercent]]
df_Count = df_Count.sort_values(['Total'], ascending=False)
 
print(df_Count)

The 7^th line adds a specific value in the results data-frame to target the wrong empty values calculation (AZERTY).

The 12 and 13^th count the empty values and its percentage aside, and store results in specific variables.

Then the 15^th line replaces the empty values calculation with our new value and variables, using loc to identify the line to fix.

Finally we can sort our data-frame with sort_values(['Total'].

Work with dates

Get true date format from string data

Example, you have dates like 21/09/2022 but you would a true date format to work with it:

df['TrueDate'] = pd.to_datetime(df['OriginalDate'], infer_datetime_format=True).dt.strftime('%Y-%m-%d')

Group by months

If you have a data-frame df with a field Created at containing dates like:

2021-02-05 10:34:21 UTC

You can work with it, example to group and sort by month, you can cheat doing:

df_Temp = pd.DataFrame(df['Created at'].str[:7])
df_Created_count = pd.DataFrame(df_Temp.groupby(['Created at'], dropna=False).size(), columns=['Total']).sort_values(['Created at'], ascending=True).reset_index()

indeed with str[:7] we extract years and months from strings. Then a usual groupby.

Ok but it is more a pandas-way to convert your field in a true date format, in a new virtual field. Then you can extract real months and store them in a proper data-frame.

df['Created'] = pd.to_datetime(df['Created at'])
df['Created'] = df['Created'].dt.to_period("M")
df_TempCreated = pd.DataFrame(df['Created'])

Then easy to group and count them.

df_Created_count = pd.DataFrame(df_TempCreated.groupby(['Created'], dropna=False).size(), columns=['Total']).sort_values(['Created'], ascending=True).reset_index()

As now you use a real date format, you can display the full months with dt.strftime.

df_Created_count['Created'] = df_Created_count['Created'].dt.strftime('%B %Y')

Format date

If you get date like 2022-10-24 and you would 24/10/2022:

df['My date'] = pd.to_datetime(df['My date'], format='%Y-%m-%d')
df['My date'] = df['My date'].dt.strftime("%d/%m/%Y")

Format date from french format

If you get date like 11/05/2023 and you would 2023-05-11:

df['My date'] = df['French date'].str.split('/').str[2] + '-' + df['French date'].str.split('/').str[1] + '-' + df['French date'].str.split('/').str[0]

Get simple date (DDDDMMDD)

# SIMPLE DATE
df_DataUnion['date_simple'] = df_DataUnion['date_submit'].astype(str).str[:10]
df_DataUnion['date_simple'] = df_DataUnion['date_simple'].replace({'-': ''}, regex=True)

Extract the previous month

PreviousMonth = today - pd.DateOffset(months=1)
print(PreviousMonth.strftime("%Y-%m"))

Convert false date field from Excel

Excel can concatenate true date format and string in the same column, arrgh! Later when you would use it in Pandas...

I use this tip to manage these kind of malicious fields in pandas:

df.loc[~df_GDC['date'].str.contains(' ', na=False), 'date'] = df['date'].astype(str).str.split(expand=True)[0]
df.loc[~df_GDC['date'].str.contains(' ', na=False), 'date'] = \
    df['date'].astype(str).str.split('-', expand=True)[2] + '/' +\
    df['date'].astype(str).str.split('-', expand=True)[1] + '/' +\
    df['date'].astype(str).str.split('-', expand=True)[0]

Eventually I put the code above in a try:... except:...

Eventually I convert all my field in string before, with astype(str)

Manage numeric date from Excel

Sometimes dates from Excel come as integer, arrgghhh!

import xlrd
 
def read_date(date):
    return xlrd.xldate.xldate_as_datetime(date, 0)
 
df.loc[df['My date'].astype(str).str.isdigit() == True, 'My date'] = \
pd.to_datetime(df['My date'].loc[df['My date'].astype(str).str.isdigit()].astype(int).apply(read_date), errors='coerce')

Check date format

Sometimes (especilally when it comes from Excel...) it is good to check your dates after treatments.

Below we search dates not starting with DD/MM/YYYY:

df2 = df[~df['my date'].str.contains('^[0-9][0-9]/[0-9][0-9]/[0-9][0-9][0-9][0-9]', regex=True)]
 
print(tabulate(df2, headers='keys', tablefmt='psql', showindex=False))

Check if a date is in the future

There are many ways, but here converting in number:

from datetime import date, datetime
 
today = date.today()
 
df_ProblemeDate = df[['My date']][pd.to_numeric(df['My date'].str.split('/').str[2] + df['My date'].str.split('/').str[1] + df['My date'].str.split('/').str[0]) > int(str(today).replace('-', ''))]

Count opening days with the same date column between the 2 following lines 

Imagine each line of your dataframe is a delivery, with a Date field:

import pandas as pd
from tabulate import tabulate
 
from datetime import date
import numpy as np
import datetime as dt
 
import warnings
from pandas.core.common import SettingWithCopyWarning
warnings.simplefilter(action='ignore', category=SettingWithCopyWarning)
...
df['Year'] = df['Date'].str.split('/').str[2]
df['Month'] = df['Date'].str.split('/').str[1]
df['Day'] = df['Date'].str.split('/').str[0]
 
df['Opening days'] = ''
 
df.reset_index(drop=True, inplace=True)
 
for i, row in df.iterrows():
    dateDeliveryYear = int(row['Year'])
    dateDeliveryMonth = int(row['Month'])
    dateDeliveryDay = int(row['Day'])
 
    if i == 0:
        df.at[i, 'Opening days'] = 'First delivery'
 
    if i > 0:
        beforeDateDeliveryYear = int(df.iloc[[i-1]]['Year'].item())
        beforeDateDeliveryMonth = int(df.iloc[[i-1]]['Month'].item())
        beforeDateDeliveryDay = int(df.iloc[[i-1]]['Day'].item())
 
        start = dt.date(beforeDateDeliveryYear,beforeDateDeliveryMonth,beforeDateDeliveryDay)
        end = dt.date(dateDeliveryYear,dateDeliveryMonth,dateDeliveryDay)
 
        df.at[i, 'Opening days'] = np.busday_count(start, end)
 
print('\n' + u)
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=True))
print(df.shape[0])

 

Work with phones

phonenumbers library

If you have a phone field and a country code field:

...
import phonenumbers
...
 
df['Phone'] = df['Phone'].fillna('')
df['CountryCode'] = df['CountryCode'].fillna('')
 
df['TestPhone'] = df.apply(
    lambda x: phonenumbers.parse(str(x['Phone']), str(x['CountryCode']))
    if str(x['Phone']) != ''
    and str(x['CountryCode']) != ''
    else '',
    axis='columns'
)

Full phone fix

# FIX PHONE
df['Phone'] = df['Phone'].str.strip()
df['Phone'] = df['Phone'].replace({'-': ' '}, regex=True)
df['Phone'] = df['Phone'].replace({'_': ' '}, regex=True)
df['Phone'] = df['Phone'].replace({'\.': ' '}, regex=True)
df['Phone'] = df['Phone'].replace({',': ' '}, regex=True)
df['Phone'] = df['Phone'].replace({' ': ' '}, regex=True)
df['Phone'] = df['Phone'].replace({'[a-zA-Z]': ''}, regex=True)
df['Phone'] = df['Phone'].replace({' ': ' '}, regex=True)
df['Phone'] = df['Phone'].replace({'^ ': ''}, regex=True)
df['Phone'] = df['Phone'].replace({'\*': ''}, regex=True)
df['Phone'] = df['Phone'].replace({'\(': ''}, regex=True)
df['Phone'] = df['Phone'].replace({'\)': ''}, regex=True)
df['Phone'] = df['Phone'].replace({'\$': ''}, regex=True)
df['Phone'] = df['Phone'].str.strip()
 
df['Phone'] = df['Phone'].fillna('')
 
df.loc[df['Phone'].str.replace(' ', '').str.len() < 4, 'Phone'] = ''
df['CountryCode'] = df['CountryCode'].fillna('')
 
# CREATE TESTPHONE FIELD
df['TestPhone'] = ''
df = df.reset_index(drop=True)
for i, row in df.iterrows():
    myPhone = row['Phone']
    myCountryCode = row['CountryCode']
    MyTest = ''
 
    try:
        # IF PHONE EXISTS AND START WITH "+" AND COUNTRY EXISTS
        if myPhone != '' and myPhone.startswith('+') and myCountryCode != '':
            MyTest = phonenumbers.parse(myPhone, myCountryCode)
 
        # IF PHONE EXISTS AND NOT START WITH "+" AND COUNTRY EXISTS
        if myPhone != '' and not myPhone.startswith('+') and myCountryCode != '':
            MyTest = phonenumbers.parse(myPhone, myCountryCode)
 
        # IF PHONE EXISTS AND START WITH "+" AND COUNTRY NOT EXISTS
        if myPhone != '' and myPhone.startswith('+') and myCountryCode == '':
            MyTest = phonenumbers.parse(myPhone, myCountryCode)
 
        # IF PHONE EXISTS AND NOT START WITH "+" AND COUNTRY NOT EXISTS
            if myPhone != '' and not myPhone.startswith('+') and myCountryCode == '':
            MyTest = phonenumbers.parse('+' + myPhone, myCountryCode)
 
    except:
        pass
 
    df.at[i,'TestPhone'] = MyTest
 
df['AutoCode'] = ''
df.loc[df['TestPhone'] != '', 'AutoCode'] = '+' + df['TestPhone'].astype(str).replace({'Country Code: ': ''}, regex=True).replace({'( National Number: ).*': ''}, regex=True)
 
df['AutoNationalPhone'] = ''
df.loc[df['TestPhone'] != '', 'AutoNationalPhone'] = df['TestPhone'].astype(str).replace({'^.*? National Number: ': ''}, regex=True).astype(str).replace({'( Leading Zero).*': ''}, regex=True)
 
# CONCATENATION
df['Formatted Phone'] = df[['AutoCode', 'AutoNationalPhone']].apply(lambda x: ' '.join(x.dropna()), axis=1).fillna('')
 
df.loc[~df['Formatted Phone'].str.startswith(('+')), 'Formatted Phone'] = df['Phone']
 
# AUTOCOUNTRYCODE
df['AutoCountryCode'] = ''
df = df.reset_index(drop=True)
 
for i, row in df.iterrows():
    myCountryCode = row['CountryCode']
    MyTest = ''
 
    try:
        MyTest = phonenumbers.country_code_for_region(myCountryCode)
    except:
        pass
 
    df.at[i,'AutoCountryCode'] = MyTest
 
# LAST
df.loc[(~df['Formatted Phone'].str.startswith(('+'))) &amp; (df['AutoCountryCode'] != 0) &amp; (df['Phone'] != ''), 'Formatted Phone'] = '+' + df['AutoCountryCode'].astype(str) + ' ' + df['Phone'].replace({'^0': ''}, regex=True)
 
# IS_POSSIBLE_NUMBER
df['is_possible_number'] = ''
for i, row in df.iterrows():
    myPhone = row['Formatted Phone']
    MyTest = ''
    try:
        MyTest = phonenumbers.is_possible_number(phonenumbers.parse(myPhone, None))
    except:
        pass
 
    df.at[i, 'is_possible_number'] = MyTest
 
# IS_VALID_NUMBER
df['is_valid_number'] = ''
for i, row in df.iterrows():
    myPhone = row['Formatted Phone']
    MyTest = ''
    try:
        MyTest = phonenumbers.is_valid_number(phonenumbers.parse(myPhone, None))
    except:
        pass
 
    df.at[i, 'is_valid_number'] = MyTest
 
# DELETE WRONG NUMBER
df.loc[(df['is_valid_number'] == False) &amp; (df['is_possible_number'] == False), 'Formatted Phone'] = ''
 
# RE-ORDER
df = df[['Phone', 'CountryCode', 'Formatted Phone']]
 
print(tabulate(df.head(30), headers='keys', tablefmt='psql', showindex=False))

 

Pie Chart

Simple pie

If you have a data-frame with fields Categories and Total, you can create a pie chart.

import matplotlib.pyplot as plt
 
chartLabel = df_Categories_count['Categories'].tolist()
chartValue = df_Categories_count['Total'].tolist()
 
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
 
fig1 = plt.figure()
plt.pie(chartValue, labels=chartLabel, colors=colors, autopct=None, shadow=False, startangle=90)
plt.axis('equal')
plt.title('Categories', pad=20, fontsize=15)
 
plt.show()
plt.clf()

Display percentages in the chart

Use autopct.

plt.pie(chartValue, labels=chartLabel, colors=colors, autopct='%1.1f%%', shadow=False, startangle=90)

Change the labels font size

plt.rcParams['font.size'] = 5

Hide a label in chart

Just replace the value with an empty string.

chartLabel[-1] = ''

Add a legend in your chart

plt.legend(chartLabel, loc='best', fontsize=8)

Explode

To explode your chart, you need to pass values in explode, example if you have 4 values to plot:

plt.pie(myValue, labels=myLabels, colors=colors, explode=[0.05, 0.05, 0.05, 0.05], autopct=None, shadow=False, startangle=90)

If you want explode all part by default, just create a list before:

explodeValues = []
for i in myValue:
    explodeValues.append(0.05)
 
plt.pie(myValue, labels=myLabels, colors=colors, explode=explodeValues, autopct=None, shadow=False, startangle=90)

Add a legend with several fields

For example you want display labels and percentages in the legend. First calculate percentages in another field in your data-frame, then:

import matplotlib.pyplot as plt
 
chartLabel = df_Categories_count['Categories'].tolist()
chartLegendLabel = df_Categories_count['Categories'].tolist()
chartValue = df_Categories_count['Total'].tolist()
chartLegendPercent = df_Categories_count['Percent'].tolist()
 
legendLabels = []
for i, j in zip(chartLegendLabel, map(str, chartLegendPercent)):
    legendLabels.append(i + ' (' + j + ' %)')
 
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
 
fig1 = plt.figure()
plt.pie(chartValue, labels=chartLabel, colors=colors, autopct=None, shadow=False, startangle=90)
plt.axis('equal')
 
plt.title('Categories', pad=20, fontsize=15)
plt.legend(legendLabels, loc='best', fontsize=8)
 
plt.show()
plt.clf()

Bar chart

Vertical bar chart

chartLabel = df_Created_count['Labels field'].tolist()
chartValue = df_Created_count['Values field'].tolist()
 
fig5 = plt.figure(figsize=(13,6))
bar_plot = plt.bar(chartLabel, chartValue)
 
# plt.ylabel('yyy')
# plt.xlabel('xxx')
plt.xticks(rotation=30, ha='right')
 
# HIDE BORDERS
plt.gca().spines['left'].set_color('none')
plt.gca().spines['right'].set_color('none')
plt.gca().spines['top'].set_color('none')
 
# HIDE TICKS
plt.tick_params(axis='y', labelsize=0, length=0)
plt.yticks([])
 
# ADD VALUE ON THE END OF HORIZONTAL BARS
# for index, value in enumerate(chartValue):
#     plt.text(value, index, str(value))
 
# ADD VALUE ON THE TOP OF VERTICAL BARS
def autolabel(rects):
    for idx, rect in enumerate(bar_plot):
        height = rect.get_height()
        plt.text(rect.get_x() + rect.get_width()/2, height, chartValue[idx], ha='center', va='bottom', rotation=0)
 
autolabel(bar_plot)
 
plt.title('Registrations by month', pad=20, fontsize=15)
 
plt.show()
plt.clf()

Work with shapefiles (without Geopandas)

Here we will use numpy and basemap.

pip install numpy
pip install basemap

To install basemap on WIndows, download a package here, according your versions, and install it with wheel.

Thanks to ramiro.org. I got the code below on his website, I just adapted it.

Suppose you have an Excel file with a country field, you get count and map them with a country shape. The country names in the Excel file and in the shape must be the same.

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
 
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from mpl_toolkits.basemap import Basemap
 
from PIL import Image, ImageOps
 
shp_simple_countries = r'C:/Users/Downloads/simple_countries/simple_countries'
 
inputExcelFile = r'C:/Users/Downloads/My file.xslx'
 
df = pd.read_excel(inputExcelFile, sheet_name='Export', engine='openpyxl', usecols=['ID', 'My Country Field'])
 
# Count the countries
df_Country_count = pd.DataFrame(df.groupby(['My Country Field'], dropna=True).size(), columns=['Total']).sort_values(['Total'], ascending=False).reset_index()
df_Country_count = df_Country_count.fillna('Unknow')
 
df_Country_count['Percent'] = (df_Country_count['Total'] / df_Country_count['Total'].sum()) * 100
df_Country_count['Percent'] = df_Country_count['Percent'].round(decimals=2)
 
# Set countries as index
df_Country_count.set_index('My Country Field', inplace=True)
 
my_values = df_Country_count['Percent']
 
num_colors = 30
cm = plt.get_cmap('Blues')
 
scheme = [cm(i / num_colors) for i in range(num_colors)]
my_range = np.linspace(my_values.min(), my_values.max(), num_colors)
 
# -1 TO AVOID SEARCHS IN A PANDAS DATA-FRAME INCLUDING START AND STOP VALUE (I think ...)
df_Country_count['Percent'] = np.digitize(my_values, my_range) - 1
 
map1 = plt.figure(figsize=(14, 8))
ax = map1.add_subplot(111, frame_on=False)
 
map1.suptitle('Countries', fontsize=30, y=.95)
 
m = Basemap(lon_0=0, projection='robin')
m.drawmapboundary(color='w')
 
m.readshapefile(shp_simple_countries, 'units', color='#444444', linewidth=.2, default_encoding='iso-8859-15')
 
# Create the chloro map
for info, shape in zip(m.units_info, m.units):
    shp_ctry = info['COUNTRY_HB']
    if shp_ctry not in df_Country_count.index:
        color = '#dddddd'
    else:
        color = scheme[df_Country_count.loc[shp_ctry]['Percent']]
 
    patches = [Polygon(np.array(shape), True)]
    pc = PatchCollection(patches)
    pc.set_facecolor(color)
    ax.add_collection(pc)
 
# Cover up Antarctica so legend can be placed over it
ax.axhspan(0, 1000 * 1800, facecolor='w', edgecolor='w', zorder=2)
 
# Draw color legend
ax_legend = map1.add_axes([0.2, 0.14, 0.6, 0.03], zorder=3)
cmap = mpl.colors.ListedColormap(scheme)
 
cb = mpl.colorbar.ColorbarBase(ax_legend, cmap=cmap, ticks=my_range, boundaries=my_range, orientation='horizontal')
cb.ax.set_xticklabels([str(round(i, 1)) for i in my_range])
cb.ax.tick_params(labelsize=7)
cb.set_label('Percentage', rotation=0)
#cb.remove()
 
# Set the map footer
# description = 'Bla bla bla'
# plt.annotate(description, xy=(-.8, -3.2), size=14, xycoords='axes fraction')
 
map1.savefig('C:/Users/Downloads/mymap1.png', dpi=100, bbox_inches='tight')
 
plt.show()
plt.clf()
 
im = Image.open('C:/Users/Downloads/mymap1.png')
bordered = ImageOps.expand(im, border=1, fill=(0, 0, 0))
bordered.save('C:/Users/Downloads/mymap1.png')

Zoom on France

map1.suptitle('Académies', fontsize=20, y=.87)
 
m = Basemap(projection='merc', resolution='l', \
llcrnrlon=-7, # Longitude minimale : étend vers l'est \
llcrnrlat=39.5, # Latitude minimale : étend vers le sud \
urcrnrlon=13, # Longitude maximale : étend vers l'ouest \
urcrnrlat=52) # Latitude maximale : étend vers le nord

Add labels or POI

ax.text(0.05, # Vers la droite
0.59, # Vers le haut
'Guadeloupe', fontsize=10, ha='center', transform=ax.transAxes)

Another example

If you use the shape attached named France-Departements-Deformation.shp:

############## Carte df_DepEtablissement
# Set academies as index
df_DepEtablissement.set_index('nom_departement_etablissement', inplace=True)
my_values = df_DepEtablissement['Pourcentage']
num_colors = 30
cm = plt.get_cmap('Blues')
scheme = [cm(i / num_colors) for i in range(num_colors)]
my_range = np.linspace(my_values.min(), my_values.max(), num_colors)
 
# -1 TO AVOID SEARCHS IN A PANDAS DATA-FRAME INCLUDING START AND STOP VALUE (I think ...)
df_DepEtablissement['Pourcentage'] = np.digitize(my_values, my_range) - 1
 
map2 = plt.figure(figsize=(14, 10))
ax = map2.add_subplot(111, frame_on=False)
 
map2.suptitle('Départements', fontsize=20, y=.87)
 
m = Basemap(projection='merc', resolution='l', 
llcrnrlon=-9, \ # Longitude minimale : étend vers l'est 
llcrnrlat=39.5, \ # Latitude minimale : étend vers le sud 
urcrnrlon=15, \ # Longitude maximale : étend vers l'ouest
urcrnrlat=52) \ # Latitude maximale : étend vers le nord
 
m.drawmapboundary(color='w')
 
m.readshapefile(shp_departements, 'units', color='#444444', linewidth=.2, default_encoding='utf-8')
 
# Create the chloro map
for info, shape in zip(m.units_info, m.units):
    shp_departements = info['dep_name']
    if shp_departements not in df_DepEtablissement.index:
        color = '#dddddd'
    else:
        color = scheme[df_DepEtablissement.loc[shp_departements]['Pourcentage']]
    patches = [Polygon(np.array(shape), True)]
    pc = PatchCollection(patches)
    pc.set_facecolor(color)
    ax.add_collection(pc)
 
# Cover up Antarctica so legend can be placed over it
# ax.axhspan(0, 1000 * 1800, facecolor='w', edgecolor='w', zorder=2)
 
# Draw color legend
ax_legend = map2.add_axes([0.2, 0.14, 0.6, 0.03], zorder=3)
cmap = mpl.colors.ListedColormap(scheme)
cb = mpl.colorbar.ColorbarBase(ax_legend, cmap=cmap, ticks=my_range, boundaries=my_range, orientation='horizontal')
cb.ax.set_xticklabels([str(round(i, 1)) for i in my_range])
cb.ax.tick_params(labelsize=7)
cb.set_label('Pourcentage', rotation=0)
# cb.remove()
 
ax.text(0.125, 0.565,'Guadeloupe', fontsize=10, ha='center', transform=ax.transAxes)
 
ax.text(0.175, 0.46, 'Martinique', fontsize=10, ha='center', transform=ax.transAxes)
 
ax.text(0.1, 0.18, 'Guyane', fontsize=10, ha='center', transform=ax.transAxes)
 
ax.text(0.42, 0.155, 'Mayotte', fontsize=10, ha='center', transform=ax.transAxes)
 
ax.text(0.6, 0.155, 'La Réunion', fontsize=10, ha='center', transform=ax.transAxes)
 
ax.text(0.73, 0.15, 'Corse', fontsize=10, ha='center', transform=ax.transAxes)
 
ax.text(0.83, 0.41, 'Nouvelle-Calédonie', fontsize=10, ha='center', transform=ax.transAxes)
 
ax.text(0.8, 0.515, 'Polynésie Française', fontsize=10, ha='center', transform=ax.transAxes)
 
ax.text(0.836, 0.69, 'Inconnu', fontsize=8, ha='center', transform=ax.transAxes)
 
ax.text(0.86, 0.903, 'Étranger', fontsize=8, ha='center', transform=ax.transAxes)
 
map2.savefig(downloadsDir + 'mymap2.png', dpi=80, bbox_inches='tight')
 
# plt.show()
# plt.clf()
 
im = Image.open(downloadsDir + 'mymap2.png')
bordered = ImageOps.expand(im, border=1, fill=(0, 0, 0))
bordered.save(downloadsDir + 'mymap2.png')
 
# INSERT IN EXCEL
img = openpyxl.drawing.image.Image(downloadsDir+'mymap2.png')
img.anchor = 'E4'
workbook['Départements établissements'].add_image(img)
workbook.save(statsFile)
 
################# REMOVE PICTURES
os.remove(downloadsDir + 'mymap2.png')

 

Geopandas

Read a shape

import geopandas as gpd
from tabulate import tabulate
 
myShape = 'C:\\Path\\Of\\My\\Shape.shp'
 
print('\n' + myShape)
 
df = gpd.read_file(myShape)
df['type'] = df['geometry'].astype(str).str.replace(r' .*|\(.*', '', regex=True)
df = df[['id', 'type', 'geometry']]
 
print(tabulate(df.head(10), headers='keys', tablefmt='psql', showindex=True))
print(df.shape[0])
 
MyFieldList = df['id'].drop_duplicates().dropna().sort_values(ascending=False).tolist()
print('\n' + str(MyFieldList))
 
MyGeomTypeList = df['type'].drop_duplicates().dropna().sort_values(ascending=False).tolist()
print('\n' + str(MyGeomTypeList))

Display a map

from tabulate import tabulate
import matplotlib.pyplot as plt
 
MyLayer = 'E:\\Path\\to\\shape.shp'
df = gpd.read_file(MyLayer)
 
df.plot()
plt.title('My layer', pad=10, fontsize=10)
plt.show()

Display the Coordinate Reference Systems (CRS)

print('roj : ' + str(df.crs))

Check geometry

df['valid ?'] = df.is_valid
df = df[df['valie ?'] == False]
 
print(tabulate(df.head(5), headers='keys', tablefmt='psql', showindex=False))
print(df.shape[0])

Create an empty shape

import geopandas as gpd
 
myShape = 'C:\\Path\\beautiful shape.shp'
schema = {"geometry": "Polygon", "properties": {"myid": "int"}}
crs = "EPSG:2154"
dfEmpty = gpd.GeoDataFrame(geometry=[])
dfEmpty.to_file(myShape, driver='ESRI Shapefile', schema=schema, crs=crs)

 

Joins

SQL JOIN LEFT

df_Join = pd.merge(df1, df2, left_on='Email', right_on='email', how='left')[['Email', 'source']]

Another example:

df_Merged = df1.merge(df2, on='id', how='left')

Another example to find the non-matching records on 2 fields:

df_join = pd.merge(df1, df2, left_on=['id1', 'field1'], right_on=['id2', 'field2'], how='left')[['id1', 'field1', 'field2']]
df_NoMatch = df_join[df_join['field2'].isna()]

 

SQL INNER JOIN

df_Join = pd.merge(df1, df2, on='Email')

Or:

df_Merged = df1.merge(df2, on='id', how='inner')

Find difference

df_difference = pd.DataFrame(df_First[~df_First['Email'].isin(df_Second['email'])])

 

Group, count and sort

List unique values

print(df_Deduped.devise.unique())

Count rows

number = df.shape[0]
print(number)

Count values (group by)

If you have a field named Country in a data-frame named df:

CountCountry = df.groupby(['Country'], dropna=False).size().sort_values(ascending=False)
print(CountCountry)

Count values (group by) and get it in a dataframe

CountCommune = pd.DataFrame(df.groupby(['id_commune'], dropna=False).size(), columns=['Total']).sort_values(['Total'], ascending=False).reset_index()
 
print(tabulate(CountCommune.head(10), headers='keys', tablefmt='psql', showindex=False))

Count values grouping by several fields to find duplicates

df_Duplicates = pd.DataFrame(df.groupby(['id_commune', 'PAGES'], dropna=False).size(), columns=['Total'])\
.sort_values(['Total'], ascending=False).reset_index()
df_Duplicates = df_Duplicates[(df_Duplicates['Total'] > 1)]
 
print(tabulate(df_Duplicates.head(10), headers='keys', tablefmt='psql', showindex=False))

Group concat

df = df.groupby(['country'])['city'].apply(lambda x: ', '.join(x)).reset_index()

Count a specific value

print (len(df[df['Your field'] == 'United States']))

Count with condition

myCount = df[df['Field where search the condition'] == 'A value'].count()['Field to count']

Count empty values

number = df['Your field'].isna().sum()

Keep empty values in a group by and fill them

newDf = pd.DataFrame(df.groupby(['Your field'], dropna=False).size(), columns=['Total']).sort_values(['Total'], ascending=False).reset_index()
newDf = newDf.fillna('Unknow')

Count email domains (group by)

You have a Email field with clean emails. Thanks to Pandas it is easy to group and count their DNS.

First create a virtual field to split the emails and recover the 2nd part of the split. Then just group your new field.

df['Domain'] = df['Email'].str.split(<a href="mailto:'@').str">'@').str</a>[1]
df_Email_DNS_count = pd.DataFrame(df.groupby(['Domain']).size(), columns=['Total'])\
    .sort_values(['Total'], ascending=False).reset_index()

Calculate percentages

Acording your needs, you can use the sum from a Total field.

df['Percent'] = (df_SpecialtiesPerCountry_count['Total'] / df['Total'].sum()) * 100
df['Percent'] = df['Percent'].round(decimals=2)

Or the number of rows in your data-frame (useful if you work with a multi-valued field, see above the Analyze data from a multi-valued field chapter) .

df['Percent'] = (df['Total'] / df.shape[0]) * 100
df['Percent'] = df['Percent'].round(decimals=2)

Sum group by year

df['Year'] = df['DateInvoice'].dt.to_period('Y')
 
df_AmountYear = df.groupby(['Year'], dropna=False)['AmountEuro'].sum().reset_index()
df_AmountYear = df_AmountYear.sort_values(['Year'], ascending=False)
 
print('\nAmount per year:')
print(tabulate(df_AmountYear, headers='keys', tablefmt='psql', showindex=False))

Sort on a field 

df.sort_values(by=['Field'], ascending=False, inplace=True)
# Recreate the index if necessary (if you will use the index later)
df = df.reset_index(drop=True)

Sort on a field containing both text and numerical values

df = df.loc[pd.to_numeric(df['My field'], errors='coerce').sort_values().index]

Sum and count

If you want sum an Amount field per country counting the number of lines:

df_AmountCountries = df.groupby(['Country'], dropna=False)\
    .agg({'ID': 'count', 'AmountEuro': 'sum'})\
    .reset_index()\
    .rename(columns={'ID': 'Number'})

 

Replace, remove, edit with conditions

Replace empty cells

df['speciality'] = df['speciality'].fillna('Other')

Replace a value (the full value)

df['Your field'] = df['Your field'].replace(['Old value'],'New value')

Replace a part of string

df['Your field'] = df['Your field'].replace({'Old value': 'New value'}, regex=True)

Replace a string if it contains

df.loc[df['speciality'].str.contains('Researcher'), 'speciality'] = 'Research Scientist'

If not contains

Add ~:

df.loc[~df['speciality'].str.contains('Researcher'), 'speciality'] = 'Research Scientist'

Localize and remove a row

ind_drop = df[df['Your field'].apply(lambda x: x == ('A value'))].index
df = df.drop(ind_drop)

Localize and remove a row starting with ...

ind_drop = df[df['Your field'].apply(lambda x: x.startswith('A value'))].index
df = df.drop(ind_drop)

Localize and remove a row ending with ...

ind_drop = df[df['Your field'].apply(lambda x: x.endswith('A value'))].index
df = df.drop(ind_drop)

Localize and replace a full row

df.loc[(df['A field'] == 'TARGET')] = [[NewValue1, NewValue2, NewValue3]]

Localize a row according a regex and edit another field

df.loc[df[' Field1'].str.contains(pat='^place ', regex=True), 'Field2'] = 'Yes'

Replace a field with values from another field with a condition

df['Field1'] = np.where(df['Field1']
    .apply(lambda x: x.startswith('A string in condition...')),
    df['Field2'], df['Field1'])

Remove some first characters

Here we delete the 2 first characters if the cell starts with a comma then a space.

df['Field'] = df['Field'].apply(lambda x: x[2:] if x.startswith(', ') else x)

Keep only some first characters

df['Field'] = df['Field'].apply(lambda x: x[:10])

Remove some last characters

df['DateInvoice'] = df['DateInvoice'].apply(lambda x: x[:-4] if x.endswith(' UTC') else x)

Remove the content from a field in another field

df['NewField'] = df.apply(lambda x : x['FieldToWork'].replace(str(x['FieldWithStringToRemove']), ''), axis=1)

Or with a regex, example to remove the content only if it is at the beginning of the field:

df['NewField'] = df.apply(lambda x : re.sub('^'+str(x['StringToRemove']), '', str(x['FieldToWork'])) if str(x['FieldToWork']).startswith(str(x['StringToRemove'])) else str(x['FieldToWork']), axis=1)

Edit with a condition

Increment a field if another field is empty.

df.loc[df['My field maybe empty'].notna(), 'Field to increment'] += 1

Fill a field if a field is greater or equal to another field.

df.loc[df['Field A'] >= df['Field B'], 'Field to fill'] = 'Yes'

Edit several fields in the same time.

df.loc[df['Field A'] >= df['Field B'], ['Field A to fill', 'Field B to fill']] = ['Yes', 'No']

Edit with several conditions

Condition "AND" (&)

df.loc[(df['My field maybe empty'].notna()) & (df['An integer field'] == 1) & (df['An string field'] != 'OK'), 'Field to increment'] += 1

Please replace "&" with a simple &.

Condition "OR" (|)

df.loc[(df['My field maybe empty'].notna()) | (df['An integer field'] == 1) | (df['An string field'] != 'OK'), 'Field to fill'] = 'Yes'

Edit with IN or NOT IN condition (as SQL)

Just use isin:

df.loc[df['Id field'].isin([531733,569732,652626]), 'Filed to edit'] = 'Yes'

And for NOT IN:

df.loc[df['Id field'].isin([531733,569732,652626]) == False, 'Filed to edit'] = 'No'

Replace string beginning with

df['id_commune'] = df['id_commune'].str.replace(r'(^75.*$)', '75056', regex=True)

Not start with

~df[phone].str.startswith(('33'))

Remove letters

df['mobile'] = df['mobile'].str.extract('(\d+)', expand=False).fillna('')

Extract before or after a string

Example if Job='IT: DBA'

df['type'] = df['Job'].str.split(': ').str[0]
df['speciality'] = df['Job'].str.split(': ').str[1]

Remove all after a string

df_Files['new field'] = df_Files['old field'].str.replace("(StringToRemoveWithAfterToo).*","", regex=True)

Remove all before a string

df_Files['file'] = df_Files['file'].str.replace("^.*?_","_", regex=True)

Get in title case

df['firstname'] = df['firstname'].str.title()

Remove if contains less of n character (lenght)

df.loc[df['mobile'].str.len() < 6, 'mobile'] = ''

Remove potential spaces before and after a string (trim)

Use .str.strip(), example:

df.loc[df['My field'].astype(str).str.isdigit() == False, 'My field'] = df['My field'].astype(str).str.strip()

Remove with a function (def)

def MyDeletion():
 
    # Eventually if your df does not exist when you create the function
    # global df
 
    ind_drop = df[df['My field'].apply(lambda x: x == ('My value'))].index
    df = df.drop(ind_drop)
 
...
 
MyDeletion()

Decode HTML special char

import html
df['My field'] = df['My field'].apply(lambda x: html.unescape(x))

 

Search

Search some character getting column and row:

for row in df_ExportFeuil1.index:
    for col in df_ExportFeuil1.columns:
        cell_value = str(df_ExportFeuil1.at[row, col])
        if '\u2264' in cell_value:
            print(colored(f'Attention, signe inférieur ou égal à la ligne {row}, colonne {col} !', 'red'))

 

Filters

Field contains string

df2 = df1[df1['My field'].str.contains('AAA').fillna(False)]
 
print(tabulate(df2.head(5), headers='keys', tablefmt='psql'))

Field not contains a string

Just use ~:

df2 = df1[~df1['My field'].str.contains('AAA').fillna(False)]
 
print(tabulate(df2.head(5), headers='keys', tablefmt='psql'))

Field not empty, not NaN, not null

df_notEmpty= df[
    (df['My field'].notnull()) &
    (df['My field'].notna()) &
    (df['My field'] != '') &
    (df['My field'] != 'nan')
]

Field is a numeric value

# Function to extract numeric values
def extractNumericValue(my_text):
    my_values = re.findall(r'^-?\d+\.\d+|-?\d+$', str(my_text))
    if my_values:
        return my_values
    else:
        return None
 
# Apply
df['SearchNumericValue'] = df['My column'].apply(extractNumericValue)
 
dfResult = df[
        (df['SearchNumericValue'].notna()) &
        (~df['My column'].astype(str).str.contains('[a-zA-Z]', regex = True))
]
 
print(tabulate(dfResult.head(10), headers='keys', tablefmt='psql', showindex=False))

Field matches regex

Here we search where the field is a number with a comma as decimal separator.

dfTest = df_ImportTxt[df_ImportTxt['My field'].str.match(pat='^\d+,\d+$')==True]

Field contains element from a list

Example searching space, star and parenthesis:

MySearch = ['\*', ' ', '\(', '\)']
 
df2 = df1[df1['zone'].str.contains('|'.join(MySearch)).fillna(False)]
 
print(tabulate(df2.head(40), headers='keys', tablefmt='psql', showindex=False))

Field equals element from a list

MySearch = ['Bbbb', 'XXX']
 
df2 = df1[df1['zone'].str.fullmatch('|'.join(MySearch)).fillna(False)]
 
print(tabulate(df2.head(40), headers='keys', tablefmt='psql', showindex=False))

Field in digit

df2 = df1.loc[df1['zone'].astype(str).str.isdigit()]
 
print(tabulate(df2.head(5), headers='keys', tablefmt='psql'))

Several condition

Below we get lines with a field with null values and another field with empty values:

df2 = df1[(df1['My field'].isna()) | (df1['My other field'] == '')]
 
print(tabulate(df2.head(5), headers='keys', tablefmt='psql', showindex=False))

Get lines searching if values are IN a list

searchIn= ['AAA', 'BBB', 'CCC']
 
df2 = df1[df1['My field'].isin(searchIn)]
 
print(tabulate(df2.head(5), headers='keys', tablefmt='psql', showindex=False))

Field not finishes with a concatenation from other fields

Suppose you have this dataframe df:

Language	PDF
Python	docA-Python.pdf
SQL	docF-SQ.pdf
PHP	docS-PHP.pdf
CSS	docG-CSS.pdf
Javascript	docR-Javascript.pdf
HTML	docW-HTML.pdf

 

Now you would like extract lines where the PDF field does not finish with [Language field] + '.pdf':

Compare = df[['Language', 'PDF']].apply(lambda x: x['PDF'].endswith(x['Language'] + '.pdf'), axis=1)
 
df_temp = pd.DataFrame(Compare)
df_temp.columns =['True or false']
 
df_concat = pd.concat([df, df_temp], axis=1)
df_filtered = df_concat[['Language', 'PDF']][df_concat['True or false'] == False]
 
print(tabulate(df_filtered.head(5), headers='keys', tablefmt='psql', showindex=False))

Language	PDF
SQL	docF-SQ.pdf

Field not contains an email 😀

...
df_pb_Email = df[df['Email field'].str.contains(r'[^@]+@[^@]+\.[^@]+') == False]
...

Field not equals an email

...
df_pb_Email = df[df['Email field'].str.fullmatch(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b') == False]
...

Field not contains a number

dfProblem = df[(df['My field'].astype(str).str.match(pat=r'^[-+]?[0-9]+$|^[-+]?[0-9]+\.[0-9]+$') == False)]

Field contains another field

df['A contains B'] = [x[0] in x[1] for x in zip(df['FieldB'], df['FieldA'])]
df= df[df['A contains B'] == False]

Field ends like another field

df['A finish with B'] = [x[1].endswith(x[0]) for x in zip(df['FieldB'], df['FieldA'])]
df= df[df['A finish with B'] == False]

Field with lenght equals a number 

dfProbleme = df[
df['Field1'].str.len() != 7
]

Count a string in a field

dfCount = df[(df['Substitutions'].str.count('\\,') > 1)]

 

Split column and get a element

Here, splitting with a comma (str.split(',')) and get the first element (.str[0]):

df['element'] = df['Myfield'].str.split(',').str[0]

Split column without to know how many column will result

df = pd.concat([
df['id'],
df['Field1'],
df['Field1'].str.split(';', expand=True).add_prefix('Field1_'),
df['Field2'],
df['Field2'].str.split(';', expand=True).add_prefix('Field2_')
], axis=1 )

 

Warnings

Avoid indexation problems after dataframes concat

Sometimes after a dataframes concatenation, the indexes are melted and can cause some problems. Just ignore the index in your new dataframe:

df_Selections = pd.concat([firstSelection, secondSelection], axis=0, ignore_index=True)

 Avoid copy warning (example 1)

import warnings
from pandas.core.common import SettingWithCopyWarning
 
warnings.simplefilter(action='ignore', category=SettingWithCopyWarning)

 Avoid copy warning (example 2)

# Below importations, above your code
pd.options.mode.chained_assignment = None

Avoid user warning 

import warnings
 
warnings.simplefilter(action='ignore', category=UserWarning)

Avoid future warning

import warnings
 
warnings.simplefilter(action='ignore', category=FutureWarning)

Avoid VisibleDeprecationWarning

import warnings
import numpy
 
warnings.filterwarnings('ignore', category=numpy.VisibleDeprecationWarning)

 

Miscellaneous

Where is Python? Which version?

Some possibilities, for example:

import platform
print('Python version:\n', platform.python_version())
 
import sys
print('\nWhere is Python:')
print(sys.executable)
 
import sys
print('\nPython version:')
print(sys.version)

Create a dataframe from a previous dataframe (copy)

df2 = df1.copy()

Copy data from a dataframe in the clipboard

Very convenient to copy-paste quickly in a text file, CSV, Excel ...

df.to_clipboard(sep=',', index=False, header=None)

Get list from field

MyList = df['My field'].drop_duplicates().dropna().sort_values(ascending=True).tolist()
print(MyList)

Get column names in a list

listColumn = df.columns.tolist()

Dedupe

To dedupe a dataframe on an Email field keeping only the first duplicate record, use drop_duplicates.

df['Email'] = df['Email'].str.lower()
dfDeduped = df.drop_duplicates(subset=['Email'], keep='first')

Find duplicates

Use duplicated:

df2 = df1[df1.duplicated(['My field'], keep=False)]
 
print(tabulate(df2.head(10), headers='keys', tablefmt='psql', showindex=False))

Find no-duplicates

df2 = df1[~df1.duplicated(['My field'], keep=False)]
 
print(tabulate(df2.head(10), headers='keys', tablefmt='psql', showindex=False))

Export a picture from a chart

And add a border.

from PIL import Image, ImageOps
 
fig1.savefig('C:/Users/myplot.png', dpi=100)
 
im = Image.open('C:/Users/myplot2.png')
bordered = ImageOps.expand(im, border=1, fill=(0, 0, 0))
bordered.save('C:/Users/myplot2.png')

Re-organize columns

df = df[['Field 1', 'Adresse mail', 'Note algorithmique', 'Substitutions']]

Rename dataframe

New_df = pd.DataFrame(Old_df)

Rename columns

From their original name:

df.rename(columns={'Old name 1': 'New name 1', 'Old name 1': 'New name 1'}, inplace=True)

From their position:

df.columns.values[12] = 'New name'

Delete column

df.drop('My field', axis=1, inplace=True)

Format number with space thousand separator

number = df.shape[0]
print(format(number, ',').replace(',', ' '), 'customers')

Or:

f"{number_of_rows:,}".replace(',', ' ')

Ranking

To rank according to a field, from highest to lowest:

df['My rank'] = df['My field to rank'].rank(ascending=False)

Avoid duplicate:

df['My rank'] = df['My field to rank'].rank(ascending=False, method='first')

Generate a unique random integer

If you want a unique random integer from 1 included to the number of records included.

howManyRecord = df.shape[0]
df['Random number'] = np.random.choice(range(howManyRecord), howManyRecord, replace=False)
df.loc[df['Random number'] == 0, 'Random number'] = howManyRecord

Select with condition

df = df.loc[df['Field to filter'] == 'Yes']

Prevent nan

Use keep_default_na=False :

Data = pd.read_excel(inputExcelFile, sheet_name='Feuil1', engine='openpyxl', keep_default_na=False)

Get tuples from a file (in a SQL UPDATE shape)

...df.itertuples()...

Very usefull to build SQL update queries from a file with the primary key in first column :

file1 = open(MyPath+'FixQueries.sql', 'w', encoding='cp1252')
 
# ÉCRITURE DU FICHIER
for i in df.itertuples(index=False):
    file1.write('UPDATE MyTable SET ' +\
        str(i)[7:-1].split(', ', 1)[-1] +\
        " WHERE id = '" + i[0] + "' ;\n")

Use index in concatenation

df.index.astype(str)

Reset index

df.reset_index(drop=True, inplace=True)

Drop index

To drop the index, you have to use another field as index.

df = df.set_index('my field')

Open a box to enter a string and use it as a variable

import tkinter as tk
from tkinter import simpledialog
 
window = tk.Tk()
window.eval('tk::PlaceWindow . center')
window.withdraw()
repertoireCarto = simpledialog.askstring("Saisie", "Répertoire ?\t\t\t\t", parent=window)
print('Le répertoire est', repertoireCarto)

 Get some line indexes according condition

list_duplicate = df.index[df['My field'] == 'Text'].tolist()

Import external scripts

Your working script needs to know where to find the external scripts:

sys.path.append('C:\\Users\\Georges\\PycharmProjects\\Your_Directory')
 
from YourScriptWithoutExtension import SomeVariables, ...

Subprocess

Not related to Pandas but very useful to run shell command, bash, console scripts...

import subprocess
 
subprocess.run('psql -h localhost -p 5432 -d work -U postgres -c "SELECT NOW() ;"', shell=True)

And to use pgsql2shp in subprocess for shape export:

subprocess.run( \
    '''pgsql2shp -f E:\\Path\\to\\MyShape -u postgres -h localhost work "SELECT * FROM MyTable ;"''', \
    shell=False, \
    executable='E:\\Path\\to\\pgsql2shp.exe' \
)

And to use PowerShell or OGR2OGR in subprocess :

subprocess.run( \
    '''OGR2OGR...''', \
    shell=False, \
    executable='E:\\Path\\to\\powershell.exe' \
)

Strings comparison

Compare the similarity of 2 strings, to get a indicator in a new field:

import difflib
from difflib import SequenceMatcher
 
df['indicator'] = df[['Field1', 'Field2']].apply(lambda x: SequenceMatcher(lambda y: y == " ", x[0], x[1]).ratio(), axis=1)

Count time execution

import time
...
print('Time for now is %s secondes' % round(time.process_time(), 1))
timeNow = time.process_time()
...
print('Time now is %s secondes' % round(time.process_time() - timeNow, 1))
timeNow = time.process_time()
...

Include external Python code (without real importation)

...
YourFileCode = r'C:/Users/Georges/PycharmProjects/WorkEMC/YourFileCode.py'
...
exec(compile(open(YourFileCode, 'rb').read(), YourFileCode, 'exec'))

Concatenate fields without empty value

df['adresse'] = df[['Filed1', 'Filed2', 'Filed3']].apply(lambda x: ' '.join(x.dropna()), axis=1)

Force data type creating the dataframe

df = pd.read_csv(workDirectory + FileName,
sep=';',
usecols=fieldsExisting,
dtype={'firstname': 'str', 'lastname': 'str'}
)

Tips:

Sometimes when your data comes from an Excel file and contains percentages, dtype will be non-efficient because Excel stores percentages as numbers. There is a solution in the chapter Advanced read/write in Excel of this article, just search Fix Excel percentages.

Fix scientific notation for phone numbers in Pandas, Tabulate and Excel export

Example for phone numbers:

df['Mobile Phone'] = df['Mobile Phone'].astype(str)
df['Mobile Phone'] = df['Mobile Phone'].fillna('')
df['Mobile Phone'] = df['Mobile Phone'].replace(['nan'], '')
df['Mobile Phone'] = df['Mobile Phone'].apply(lambda x: x[:-2] if x.endswith('.0') else x)

Get file/directory date creation

import os
import time
from time import gmtime, strftime
 
myRep = 'C://Path/of/my/directory'
 
myDateCreation = time.ctime(os.path.getctime(myRep))
print('\n' + str(myDateCreation))
 
myConvertDateCreation = time.strptime(myDateCreation)
print("\nFull:", myConvertDateCreation)
 
print("\nDay:", myConvertDateCreation.tm_mday)
print("Month:", myConvertDateCreation.tm_mon)
print("Year:", myConvertDateCreation.tm_year)
print("Hour:", myConvertDateCreation.tm_hour)
print("Minutes:", myConvertDateCreation.tm_min)
 
print(strftime("\n%A %d %B %Y %H:%M", myConvertDateCreation ))

Use a dataframe not yet created in a function

Pass it as global:

def MyDeletion():
    global df
    ind_drop = df[df['My field'].apply(lambda x: x == ('My value'))].index
    df = df.drop(ind_drop)

Organize your screens

Attention: wait the end of the process for proper screens!

import subprocess
import clipboard
import pyautogui
import pygetwindow as gw
from screeninfo import get_monitors
 
# Callback function to find file explorer windows
def enum_windows_callback(hwnd, results):
    if win32gui.GetClassName(hwnd) == 'CabinetWClass':
        results.append(hwnd)
    return True
 
# Get all File Explorer windows
explorer_windows = []
win32gui.EnumWindows(enum_windows_callback, explorer_windows)
 
# Close every File Explorer window
for hwnd in explorer_windows:
    # Activate window
    win32gui.ShowWindow(hwnd, win32con.SW_SHOW)
    win32gui.SetForegroundWindow(hwnd)
 
    # Use pyautogui to send Alt+F4 key combination to close window
    pyautogui.hotkey('alt', 'f4')
 
# Open File Explorer
subprocess.call("start explorer", shell=True)
 
# Wait a short while for File Explorer to fully open
pyautogui.sleep(1)
 
# Select the desired screen (for example, the first screen)
monitors = get_monitors()
screen = monitors[0]# Récupérer les coordonnées du coin supérieur gauche de l'écran
left = screen.x
top = screen.y
 
# Find the File Explorer window
explorer_window = gw.getWindowsWithTitle('Explorateur de fichiers')[0]
 
# Desired position on the screen
pyautogui.sleep(0.5)
x_position = 10 # To the right
y_position = 15 # To the down
explorer_window.moveTo(x_position, y_position)
 
# Size
pyautogui.sleep(0.5)
width = 600
height = 600
explorer_window.resizeTo(width, height)
 
# Directory
pyautogui.sleep(0.5)
directory_path = 'C:\\Users\\Georges\\Downloads'
clipboard.copy(directory_path)
pyautogui.hotkey('alt', 'a')
 
# pyautogui.typewrite(directory_path)
pyautogui.hotkey('ctrl', 'v')
pyautogui.press('enter')
 
# pyautogui.click(button='right')
# subprocess.call("start notepad", shell=True)C:\Users\Georges\Downloads

Search a string in database

from tabulate import tabulate as tab
import pandas as pd
from sqlalchemy import create_engine
 
SearchWord = 'anyxxxtube'
 
username = 'XXXXX'
password = 'XXXXX'
port = 3306
database = 'XXXXX'
host = 'XXXXX'
 
engine = create_engine('mysql+mysqldb://%s:%s@%s:%i/%s' % (username, password, host, port, database))
 
# SEARCH TABLES AND FIELDS
sql = 'SELECT table_name, column_name FROM INFORMATION_SCHEMA.COLUMNS WHERE table_name LIKE "prime_%%" ;'
df = pd.read_sql_query(sql, engine)
 
print(tab(df.head(5), headers='keys', tablefmt='psql', showindex=False))
print(df.shape[0])
 
# BUILD QUERIES
Assemblage = list(zip(df['table_name'].tolist(), df['column_name'].tolist()))
# for a, b in Assemblage:
#     print(a, ' - ', b)
 
for a, b in Assemblage:
    sql = 'SELECT '' + b + '' FROM '' + a + '' WHERE '' + b + '' LIKE "%%' + SearchWord + '%%" ;'
    df = pd.read_sql_query(sql, engine)
 
    if df.shape[0] > 0:
        print('In table "' + a + '" :')
        print(tab(df.head(5), headers='keys', tablefmt='psql', showindex=False))
        print(df.shape[0])

Name dataframes dynamically

In a loop for exampe, with globals(): 

ListFiles = ['Stats Name1.xlsx', 'Stats Name2.xlsx']
 
for f inListFiles:
    dfTemp = pd.read_excel(outputDir + f, sheet_name='Sexe', engine='openpyxl')
 
    name = f.replace('.xlsx', '').replace('Stats ', '')
    globals()[name] = dfTemp
 
    print('\n' + name + ' :')
    print(tabulate(globals()[name].head(35), headers='keys', tablefmt='psql', showindex=False))